Novel species of fungi described in this study include those from various countries as follows: Angola , Gnomoniopsis angolensis and Pseudopithomyces angolensis on unknown host plants. Australia , Dothiora corymbiae on Corymbia citriodora, Neoeucasphaeria eucalypti (incl. Neoeucasphaeria gen. nov.) on Eucalyptus sp., Fumagopsis stellae on Eucalyptus sp., Fusculina eucalyptorum (incl. Fusculinaceae fam. nov.) on Eucalyptus socialis, Harknessia corymbiicola on Corymbia maculata, Neocelosporium eucalypti (incl. Neocelosporium gen. nov., Neocelosporiaceae fam. nov. and Neocelosporiales ord. nov.) on Eucalyptus cyanophylla, Neophaeomoniella corymbiae on Corymbia citriodora , Neophaeomoniella eucalyptigena on Eucalyptus pilularis, Pseudoplagiostoma corymbiicola on Corymbia citriodora, Teratosphaeria gracilis on Eucalyptus gracilis, Zasmidium corymbiae on Corymbia citriodora. Brazil , Calonectria hemileiae on pustules of Hemileia vastatrix formed on leaves of Coffea arabica , Calvatia caatinguensis on soil, Cercospora solani-betacei on Solanum betaceum , Clathrus natalensis on soil, Diaporthe poincianellae on Poincianella pyramidalis , Geastrum piquiriunense on soil, Geosmithia carolliae on wing of Carollia perspicillata , Henningsia resupinata on wood, Penicillium guaibinense from soil, Periconia caespitosa from leaf litter, Pseudocercospora styracina on Styrax sp., Simplicillium filiforme as endophyte from Citrullus lanatus , Thozetella pindobacuensis on leaf litter, Xenosonderhenia coussapoae on Coussapoa floccosa. Canary Islands (Spain) , Orbilia amarilla on Euphorbia canariensis. Cape Verde Islands , Xylodon jacobaeus on Eucalyptus camaldulensis. Chile , Colletotrichum arboricola on Fuchsia magellanica. Costa Rica , Lasiosph...

1) Wind connectivity has been identified as a key factor driving many biological processes. 2) Existing software available for managing wind data are often overly complex for studying many ecological processes and cannot be incorporated into a broad framework. 3) Here we present rWind, an R language package to download and manage surface wind data from the Global Forecasting System and to compute wind connectivity between locations. 4) Data obtained with rWind can be used in a general framework for analysis of biological processes to develop hypotheses about the role of wind in driving ecological and evolutionary patterns.

Prediction of Iberian lynx road-mortality in southern Spain: a new approach using the MaxEnt algorithm. In recent years, the Iberian lynx (Lynx pardinus) has experienced a significant increase in the size of its population and in its distribution. The species currently occupies areas in which it had been extinct for decades and new road mortality black spots have been identified. Its conservation requires an intensive risk assessment of road-deaths in potential future distribution areas. Using the MaxEnt algorithm we aimed to identify the roads where there is a greater risk of road collision for the Iberian lynx. More than 1,150 stretches of road were evaluated in Andalusia (southern Spain). Both road-related and habitat variables were included in the model. A total of 1,395 km of the 7,384 km evaluated (18.9 %) were classified as high risk road. Our results could help plan future conservation strategies. To our knowledge, this is the first time that the MaxEnt algorithm has been used to provide spatially-explicit predictions about wildlife road mortality.

Camera‐trapping methods have been used to monitor movement and behavioural ecology parameters of wildlife. However, when considering movement behaviours to estimate DR is mandatory to include in the formulation the speed ratio, otherwise DR results will be biased. For instance, some wildlife populations present movement patterns characteristic of each behaviour (e.g. foraging or displacement between habitat patches), and further research is needed to integrate the behaviours in the estimation of movement parameters. In this respect, the day range (average daily distance travelled by an individual, DR) is a model parameter that relies on movement and behaviour. This study aims to provide a step forward concerning the use of camera‐trapping in movement and behavioural ecology. We describe a machine learning procedure to differentiate movement behaviours from camera‐trap data, and revisit the approach to consider different behaviours in the estimation of DR. Second, working within a simulated framework we tested the performance of three approaches to estimate DR: DROB (i.e. estimating DR without behavioural identification), DRTB (i.e. estimating DR by identifying behaviours manually and weighting each behaviour on the basis of the encounter rate obtained) and DRRB (i.e. estimating DR based on the classification of movement behaviours by a machine learning procedure and the ratio between speeds). Finally, we evaluated these approaches for 24 wild mammal species with different behavioural and ecological traits. The machine learning procedure to differentiate behaviours showed high accuracy (mean = 0.97). The DROB approach generated accurate results in scenarios with a speed‐ratio (fast relative to slow behaviours) lower than 10, and for scenarios in which the animals spend most of the activity period on the slow behaviour. However, when considering movement behaviours to estimate DR is mandatory to include in the formulation the speed ratio, otherwise the DR results will be biased. The new approach, DRRB, generated accurate results in all the scenarios. The results obtained from real populations were consistent with the simulations. In conclusion, the integration of behaviours and speed‐ratio in camera‐trap studies makes it possible to obtain unbiased DR. Speed‐ratio should be considered so that fast behaviour is not overrepresented. The procedures described in this work extend the applicability of camera‐trap‐based approaches in both movement and behavioural ecology.

In October 2018 the ENETWILD consortium created suitability maps based on available data on wild boar occurrence at 10 km square resolution and initial version of abundance models based on hunting statistics at NUTS3 and NUTS2 resolution, that were statistically downscaled for MSs to 10x10 km grid squares. This report presents updated suitability map for wild boar presence based on additional occurrence data and new algorithms, and new models based on highresolution hunting yield data for MSs and neighbouring countries. New environmental variables closely associated with wild boar abundance and distribution were also included. Our results showed no consensus for a single best occurrence model: out of those tested, both Maxent and random forest could be considered the best options depending on the choice of assessment metric. Predictions from these models notably disagreed in eastern Europe where data on wild boar occurrence are limited. Despite agreement among models, predictions in the south appeared over-predicted, most likely due to a lack of contrasting absence data. Whilst there remain some methodological adjustments which could be tested, substantial improvement in the prediction from occurrence models relies on further collection of wild boar occurrence data in the east and complimentary data on survey effort in the south. The predictive performance of the hunting yield model was high. Although the incorporation of new data at higher spatial resolution markedly improved predictions, such data is still needed in some regions, ideally coupled with hunting effort, which would allow such estimates to be transformed into reliable densities. Comparison between predictions from the occurrence and hunting yield models showed they were statistically 1 ENETWILD Consortium: www.enetwild.com www.efsa.europa.eu/efsajournal 1 EFSA Supporting publication 2019:EN-1674Modelling wild boar distribution associated, but the strength of that relationship was dependent on the type of occurrence model and the bioregion. These findings are compatible with previous interpretations of the occurrence model, and highlight the relevance of obtaining more accurate data, especially from northern and eastern bioregions in Europe.